As we move into 2025, AI continues to transform industries in unprecedented ways, driving efficiency, innovation, and productivity. But with this rapid advancement come critical ethical questions. How can we ensure that AI systems protect the rights and well-being of individuals? 

Manufacturing and agriculture are two essential industries where answering this question is critical. Manufacturing often involves high-risk environments where safety and continuity are paramount, while agriculture impacts data privacy and community welfare, affecting the very people and lands that sustain us.

By closely examining these industries, we can see how ethical AI is considered in various contexts. Let’s explore what ethical AI means for manufacturing and agriculture and why balancing innovation and responsibility is crucial to a future where AI truly serves humanity.

Manufacturing: Ensuring ethical AI use in the age of automation and efficiency 

In manufacturing, the Fourth Industrial Revolution has brought a swath of technologies like robotics, IoT sensors, computer vision and AI-powered predictive maintenance, transforming how factories operate. From real-time safety alerts to predictive maintenance that can avoid costly breakdowns, AI is making factory floors smarter and safer.

For example, AI is helping balance the key ingredients of cheese at a Dutch manufacturer to ensure product quality and avoid preventable food waste. IoT technology is also helping save lives and increase worker safety by implementing AI-driven safety measures into floor operations, analyzing real-time streaming data, and alerting managers to potential safety violations before they lead to accidents.

But here’s the challenge: Manufacturing environments often operate 24/7, so halting operations isn’t always simple or safe. You usually can’t simply stop a system that’s having issues, as that would require potentially pausing a process or production. This could have a multi-million-dollar impact on the business and – depending on the process – could be dangerous to stop.

Imagine a manufacturing facility that processes a highly reactive chemical mixture. The reactor operates at high temperatures and pressures to ensure that chemical reactions occur efficiently. If there is an issue with the reactor, such as a malfunction or a need for maintenance, stopping the reactor suddenly can cause a rapid build-up of pressure within the system. This pressure build-up can lead to an explosion, causing significant damage to surrounding equipment and any nearby workers.

So even if the AI system can identify a maintenance issue immediately – a human worker still needs to review the issue to decide whether the repair can or should occur immediately or whether other factors require operating with a malfunction for a period of time.

Here, AI plays a unique role in helping to balance efficiency and safety without disrupting critical processes. But to do this responsibly, manufacturers need to assess the ethical risks:

• Is the AI monitoring the right metrics?
• Are contingency plans in place?
• Are workers aware of both the technology’s potential benefits and risks?

Ethical AI in manufacturing isn’t just about tech that works “efficiently and productively” but about creating a safe, reliable and continuously monitored process. Furthermore, regular audits and updates to AI systems must be conducted to ensure ongoing safety and compliance with ethical standards. Failing to do so could compromise worker safety or lead to unintended consequences.

Agriculture and conservancy: Bridging the digital divide for sustainable and fair farming 

In the quest to balance innovation with tradition, AI ethics in agriculture and conservancy demands thoughtful consideration to ensure technology enhances, rather than disrupts, the harmony between humanity and nature.

In agriculture, AI is revolutionizing farming practices through precision farming, water optimization, and enhanced crop yields. In conservancy, AI can monitor wildlife populations, track poaching activities, and manage natural resources. While these applications are promising, ethical implications need to be considered. Unlike manufacturing, the ethical concerns in agriculture focus more on transparency, privacy, and community impact.

AI systems in agriculture rely on large amounts of data – such as satellite imagery and sensor readings from farmland – which often include images as well as the commute and life patterns of farmers, workers, and local communities.

This raises concerns about data copyright and consent on farmers, who tend to be fiercely independent and naturally protective of the information that supports their livelihood.  If an AI data aggregator appropriates the grower's intellectual property and transfers the data to a third party, then the grower, the equipment manufacturer, or the countries that subsidized the technology implementation may have been infringed. AI systems often rely on vast amounts of data, including satellite imagery and farm sensor data. So, ensuring this data is collected and used transparently – and with consent – is critical to maintaining stakeholder trust. 

Ethical AI in agriculture calls for:

• Transparency: Farmers should be provided clear explanations about data collection, AI algorithms, and potential risks, such as privacy breaches.
• Consent-based data collection: Communities should have a say in how their data is used, particularly when it affects privacy, such as if a camera catches and exploits a farmer in a not-safe-for-work or embarrassing environment.
• Community impact: AI systems should not only avoid harm but actively support the well-being of farmers and local communities, prioritizing sustainable and fair practices.